000 | 04033nam a22005895i 4500 | ||
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001 | 978-3-031-01585-4 | ||
003 | DE-He213 | ||
005 | 20240730164106.0 | ||
007 | cr nn 008mamaa | ||
008 | 220601s2020 sz | s |||| 0|eng d | ||
020 |
_a9783031015854 _9978-3-031-01585-4 |
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024 | 7 |
_a10.1007/978-3-031-01585-4 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
|
072 | 7 |
_aCOM004000 _2bisacsh |
|
072 | 7 |
_aUYQ _2thema |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aYang, Qiang. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _982065 |
|
245 | 1 | 0 |
_aFederated Learning _h[electronic resource] / _cby Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu. |
250 | _a1st ed. 2020. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2020. |
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300 |
_aXVII, 189 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aSynthesis Lectures on Artificial Intelligence and Machine Learning, _x1939-4616 |
|
505 | 0 | _aPreface -- Acknowledgments -- Introduction -- Background -- Distributed Machine Learning -- Horizontal Federated Learning -- Vertical Federated Learning -- Federated Transfer Learning -- Incentive Mechanism Design for Federated Learning -- Federated Learning for Vision, Language, and Recommendation -- Federated Reinforcement Learning -- Selected Applications -- Summary and Outlook -- Bibliography -- Authors' Biographies. | |
520 | _aHow is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application. | ||
650 | 0 |
_aArtificial intelligence. _93407 |
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650 | 0 |
_aMachine learning. _91831 |
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650 | 0 |
_aNeural networks (Computer science) . _982066 |
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650 | 1 | 4 |
_aArtificial Intelligence. _93407 |
650 | 2 | 4 |
_aMachine Learning. _91831 |
650 | 2 | 4 |
_aMathematical Models of Cognitive Processes and Neural Networks. _932913 |
700 | 1 |
_aLiu, Yang. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _982067 |
|
700 | 1 |
_aCheng, Yong. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _982068 |
|
700 | 1 |
_aKang, Yan. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _982069 |
|
700 | 1 |
_aChen, Tianjian. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _982070 |
|
700 | 1 |
_aYu, Han. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _982071 |
|
710 | 2 |
_aSpringerLink (Online service) _982072 |
|
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031000300 |
776 | 0 | 8 |
_iPrinted edition: _z9783031004575 |
776 | 0 | 8 |
_iPrinted edition: _z9783031027130 |
830 | 0 |
_aSynthesis Lectures on Artificial Intelligence and Machine Learning, _x1939-4616 _982073 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-01585-4 |
912 | _aZDB-2-SXSC | ||
942 | _cEBK | ||
999 |
_c85292 _d85292 |